CNNs for deep learning
This tutorial shows you how to use multiple GPUs to train your TensorFlow neural networks. You'll learn how to use data parallelism to train your models on
A memory framework combining short-term and long-term memory in neural networks improves long-sequence modeling efficiency and performance
NeurIPS 2020 What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Review 1 Summary and Contributions: The paper considers the issue of memorization in deep neural networks, and build upon the work of Feldman [12] in this area. The authors propose a set of experiments to estimate the influence of training examples, and propose an efficient way to estimate influence from a subsampled training set. Experiments on CIFAR and Imagenet show examples of memorised images, and the
NeurIPS 2020 What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Review 1 Summary and Contributions: The paper considers the issue of memorization in deep neural networks, and build upon the work of Feldman [12] in this area. The authors propose a set of experiments to estimate the influence of training examples, and propose an efficient way to estimate influence from a subsampled training set. Experiments on CIFAR and Imagenet show examples of memorised images, and the
The text discusses the differences between neural networks and decision trees in handling data, highlighting that networks can interpolate and create new features while trees rely on memory and closest matches. It also touches on the potential of using genetic algorithms and random enumeration for model development, noting that many advancements in machine learning are quickly adopted by quantitative traders
where Innovation meets Impact
How to Define Your Neural Network Architecture Start by determining the type of neural network that best fits your problem
And a great learning tool for understanding neural nets
Optimal decision-making in social settings is often based on forecasts from time series (TS) data. Recently, several approaches using deep neural networks (DNNs) such as recurrent neural networks (RNNs) have been introduced for TS forecasting and have shown promising results. However, the applicability of these approaches is being questioned for TS settings where there is a lack of quality training data and where the TS to forecast exhibit complex behaviors. Examples of such settings include financial TS fo